fuzzy logic algorithm and analytic network process (anp

ILKOM Jurnal Ilmiah Vol. 13, No. 1, April 2021, pp. 18-30 Accredited 2nd by RISTEKBRIN No. 200/M/KPT/2020; E-ISSN 2548-7779 | P-ISSN 2087-1716

http://dx.doi.org/10.33096/ilkom.v13i1.750.18-30

18

Fuzzy logic algorithm and analytic network process

(ANP) for boarding houses searching recommendations

Wawan Gunawan Universitas Mercu Buana, Jl. Meruya Selatan No. 1, Kembangan, Jakarta Barat 11650, Indonesia

[email protected]

Article history: Received February 2, 2021; Revised April 26, 2021; Accepted April 28, 2021; Available online April 30, 2021

Keywords: Analytic Network Process; Fuzzy Logic; DSS; Boarding house; Recommendation

Introduction Currently, many employees and students from outside the city live in boarding houses because it can save time

and transportation costs. It will be exhausting for them to go back and forth from their hometown to their workplaces

and schools. The problem occurs when they want to find a boarding house that fits the criteria, as they have to search

from one boarding house to another [1]. There are certain criteria for boarding house seekers to get the right and

appropriate boarding house [2] [3]. However, limited information regarding the location, price, facilities and contact

of the boarding house owner is the common problem often faced by them.

A study used the SAW (Simple Additive Weighting) method using 3 criteria, namely a) price; b) location; c)

facilities and using 3 alternatives, obtained the best alternative output value of 3.99 [4]. Another study used the

Analytic Network Process (ANP) method for the selection of boarding houses using 3 criteria, namely a) distance; b)

price; c) facilities and using 30 alternative boarding houses obtained the result of 3,714 for Kos Putri (female boarding

house) and then 3,702 for Asenkar boarding house [5].

In addition, a study using Fuzzy Sugeno employed 6 criteria, namely price, rooms, location of the market, nearby

dining places, places of worship, and parking lots [6]. To obtain the result in this study, four stages of data collection

were conducted; they included the formation of fuzzy sets, the formation of rules, application of implication functions

and rule inference as well as defuzzification. Some of the main components in choosing the ideal house are

accessibility and flooding avoidance [7], safety and convenience standards, i.e., parking availability, safe

neighbourhood (from thefts and sexually immoral acts), adequate lighting, good air circulation, provision of clean

water and windows [8] [9].

In addition, there are several health requirements for housing needed by humans such as oxygen, availability of

clean water, air circulation, density of occupancy, disposal of waste, environmental facilities, and infrastructure as

well as reforestation [10]. Referring to previous research in finding boarding houses that used 3 criteria, namely

distance, price, and bathroom facilities, this study adds several criteria such as price, facilities, security [11], distance,

convenience, parking space, and the number of spaces. 12]. It is hoped that the addition of the criteria can produce a

system that is able to overcome the problems mentioned above.

Research Article Open Access (CC–BY-SA)

Abstract Finding a boarding house is usually done manually or by visiting the boarding house in person. Several choices of boarding houses

make boarding house seekers have to make choices according to the desired criteria, so it takes quite a long time. A decision

support system is a system that can be used to help make decisions based on existing criteria for determining several alternatives

to be selected. The methods used in this research are the Analytic Network Process (ANP) and the Fuzzy Logic method. This

study employed several criteria in providing recommendations, including distance, price, facilities, security, number of spaces,

parking space and convenience. The weighting of these criteria used the fuzzy logic method based on the priority scale determined

by the boarding house seekers. This system has provided a recommendation for boarding houses based on the results of the

calculation process using the ANP method and weighting using fuzzy logic. The result of calculations shows that the highest value

was obtained by Munawar kos (boarding house) with a value of 6.55% and followed by Diding kos with a value of 6.52%.

E-ISSN 2548-7779 ILKOM Jurnal Ilmiah Vol. 13 No. 1, April 2021, pp.18-30 19

Gunawan (Fuzzy logic algorithm and analytic network process (ANP) for boarding houses searching recommendations)

Method A. Fuzzy Logic

The weighting of the criteria to be used is based on the priority determined by the boarding house seekers so that

the weight can be determined using fuzzy logic. In this study, 7 criteria were proposed, and the boarding house seekers

could choose all of these criteria, or it could be less than 7 criteria, depending on their needs.

In this study, the criteria used include rental prices, available facilities, security, distance, convenience, parking

space and the number of spaces. These criteria can be selected by the seekers based on the order of priority, whether

the main priority is based on distance, or based on price, and so on. The priority can be described as in Figure 1.

Figure 1. Fuzzy logic weighting

The equation (1) is:

Weighting = ((𝑐𝑜𝑢𝑛𝑡 − 𝑛) + 1)𝑥(1/𝑐𝑜𝑢𝑛𝑡)𝑥100 (1)

Count = number of priority

N = priority order

B. Analytic Network Process (ANP)

The Analytic Network Process (ANP) method is a qualitative approach developed to improve the weaknesses of

the Analytical Hierarchy Process (AHP) method in the form of the ability to recommend linkages to the ANP method

[13]. The Analytical Network Process (ANP) method is a mathematical theory that is able to analyze an effect by

using an assumption approach to solve problems. This method is used with consideration of adjusting the complexity

of the problem by means of a synthesis decomposition accompanied by a priority scale that produces the greatest

priority effect. In the AHP network there are levels of objectives, criteria, sub-criteria, and alternatives, each of which

has elements. Whereas in the ANP network, the level in AHP is called a cluster which can have criteria and alternatives

in it [14]

The advantage of the ANP method when compared to AHP is in solving more complex problems [15]. Research

using the Analytic Network Process (ANP) method is the right solution in determining priority road handling based

on the level of road service. The study shows a correlation value between -1 to 1 with a value of 0.867 which has been

validated using the Spearman Rank for 10 roads in the city of Cirebon [16]. The steps used to solve this problem are:

1. Identify problems and determine solution criteria.

2. Determine the weighting of the criteria by the user

3. Create a Comparison Matrix

Comparison matrix is done by making comparisons in pairs for each hierarchical sub-system, then transforming it

in the form of a matrix for a numerical analysis process (n x n matrix). Comparison matrixe is shown in Table 1.

Table 1. Comparison matrix

A B1 B2 B3 ... Bn B1 B11 B12 B13 …. B1n

B2 B21 B22 B23 …. B2n

B3 B31 B32 B33 …. B3n

... …. …. …. …. ….

….. ….. …. ….. … ….

Bn Bn1 Bn2 Bn3 …. Bnn

4. Calculating the Eigenvector Value

20 ILKOM Jurnal Ilmiah Vol. 13 No. 1, April 2021, pp.18-30 E-ISSN 2548-7779


To calculate the eigenvector value, we added up the values in each column of the matrix then divided

each value in the column cell by the total column, then added up the values for each row and divide by the

value of n. The calculation of the eigenvector value can be seen in equation (2).

𝑋 = ∑(𝑊𝑖

∑𝑊𝑖) (2)

X = eigenvector

Wi = single row column cell value (i = 1 .... n)

∑W_i = total number of columns

5. Checking Consistency Ratio

a. Looking for the value of λmaks shown in equation (3)

λmaks = (eigenvector value 1 x number of columns 1) +

(eigenvector value 2 x number of columns 2) ... n (3)

b. Calculating the Consistency Index (CI), with equation (4).

CI = ((λmaks-n)) / ((n-1)) (4)

Where:

CI = Consistency Index

λmaks = the largest eigenvector value

n = number of comparison matrices

c. Determining Consistency Ratio (CR)

𝐶𝑅 = 𝐶𝐼 / 𝑅𝐼 (5)

Table 2. Random Index Value

Orde Matrix 1 2 3 4 5 6 7 8 9

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

CR will have a better value and can show its consistency if it is close to zero in the comparison

matrix. Random Index Value is shown in Table 2.

6. Making Supermatrix

There are 3 types of supermatrix in ANP.

a. Unweight supermatrix, the eigenvector generated from the whole pairwise comparison matrix in the

network.

b. Weighted supermatrix, multiplying the contents of the unweighted supermatrix with the cluster

weight.

c. Limit supermatrix, performing the weighted supermatrix continuously until the number in each

column in one row is the same then normalize.

7. Ranking

It is the final value in the ANP method that has been carried out by the normalization process to find out

the final value of the calculation. The best alternative is generated from the highest alternative score [16].

C. Methodology

In this study, our methodology is as shown in Figure 2.



Figure 2. Methodology

This research was conducted with the following stages:

1. Interview

The interview was conducted to know the business process in searching for boarding houses manually

by conducting questions and answers to employees and students in order to get precise and accurate

results.

2. Observation

The researcher collected data based on predetermined criteria for several boarding houses in Tangerang

area.

3. Literature/Library Studies

The data was further analyzed by studying several journals related to the search for boarding houses

including the criteria used [17].

Results and Discussion

A. Design

In this study, the boarding house seekers used 7 selected criteria with top priority starting from the price offered,

the facilities provided, the security of the boarding house whether security was available or not, the distance from the

boarding house to the place of activity, the convenience of the boarding house, the availability of parking lot both car

parking and motorbike parking, and the last priority is the number of rooms in the boarding house. Based on equation

1, the weights for each criterion were obtained as shown in Table 3.

Table 3. Weighting criteria

Priority Code Criteria Weight

1 H Price 100

2 F Facility 86

3 KA Security 71

4 J Distance 57

5 KN Convenience 43

6 P Parking lot 29

7 R Number of rooms 14

After the weight value was determined by the user, then the researcher made a pairwise comparison matrix as

shown in Table 4.

Table 4. Comparison matrix

Criteria H F KA J KN P R

H 1.00 1.16 1.41 1.75 2.33 3.45 7.14

F 0.86 1.00 1.21 1.51 2.00 2.97 6.14



Criteria H F KA J KN P R

KA 0.71 0.83 1.00 1.25 1.65 2.45 5.07

J 0.57 0.66 0.80 1.00 1.33 1.97 4.07

KN 0.43 0.50 0.61 0.75 1.00 1.48 3.07

P 0.29 0.34 0.41 0.51 0.67 1.00 2.07

R 0.14 0.16 0.20 0.25 0.33 0.48 1.00

∑ 4.00 4.65 5.63 7.02 9.30 13.79 28.57

The next step was to determine the eigenvector value using the equation (2) by adding up the value of each row

from the matrix and then dividing each value of the number of row cells by the total column. The eigenvector value

is shown in Table 5.

Table 5. Eigen value

Criteria code Total Eigen

H 18.24 0.25

F 15.69 0.22

KA 12.95 0.18

J 10.40 0.14

KN 7.84 0.11

P 5.29 0.07

R 2.55 0.04

∑ 72.97 1 00

Furthermore, after the eigenvector value is obtained, the next step was to check the consistency ratio using the

formula in equation 3, if the value is < 0.1 then results are consistent.

𝜆𝑚𝑎𝑘𝑠 = (0.25x 4.00) + (0.22 x 4.65) + (0.18 x 5.63) + (0.14 x 7.02) + (0.11 x 9.30) + (0.07 x 13.79) + (0.04

x 28.57)

= 7

Next was calculating CI with the number of orders using 7 criteria. To calculate CI the equation (4) was used.

CI = (λmax – n) / (n – 1)

= (7 – 7) / (7 – 1)

= 0.00

The Random Index (RI) used is 1.32 based on the value specified in table 2. The calculation of the CR value can

use equation (5).

CR = CI / RI

= 0.00 / 1.32

= 0.00

Because the CR result is 0.00 less than 0.10 (CR<0.1), the eigenvector value is considered consistent.

The next step was to calculate the eigenvector price using the equation (2). The first step was to calculate the

reverse value. The reverse price value is to reverse the value by subtracting the total price value by the price value of

each Kos (boarding house). After that, the eigenvector value was obtained by dividing each reserve value of Kos by

the total reverse price. Table 6 is the result of the eigenvector price calculation.

Table 6. Eigenvector Price

No Alternative Price Value Reverse Price Eigen vector

1 Mariyam Kos 850,000 13,455,000 0.0495

2 Hariyono Kos 800,000 13,505,000 0.0497

3 Deni Kos 750,000 13,555,000 0.0499



No Alternative Price Value Reverse Price Eigen vector

4 Budi Kos 650,000 13,655,000 0.0502

5 Sinta Kos 700,000 13,605,000 0.0501

6 Aldian Kos 615,000 13,690,000 0.0504

7 Doni Kos 800,000 13,505,000 0.0497

8 Munawar Kos 800,000 13,505,000 0.0497

9 Andreas Kos 580,000 13,725,000 0.0505

10 Harry Kos 850,000 13,455,000 0.0495

11 Rika Kos 550,000 13,755,000 0.0506

12 Riski Kos 700,000 13,605,000 0.0501

13 Dedi Kos 560,000 13,745,000 0.0506

14 Dede Kos 600,000 13,705,000 0.0504

15 Anang Kos 570,000 13,735,000 0.0505

16 Mariya Kos 750,000 13,555,000 0.0499

17 Slamet Kos 900,000 13,505,000 0.0493

18 Diding Kos 850,000 13,405,000 0.0495

19 Yanto Kos 880,000 13,455,000 0.0494

20 Nurul Kos 550,000 13,425,000 0.0506

∑ 14,305,000 271,795,000 1

The second criterion is that the facility that used equation (2) without using the reverse process, because the higher

the facility value, the better. The calculation process is the value of each facility divided by the total value of all Kos

facilities. Table 7 is the result of the calculation of the eigenvector facility.

Table 7. Eigenvector Facility

No Alternative Facility value Eigen vector

1 Mariyam Kos 4 0.0727

2 Hariyono Kos 4 0.0727

3 Deni Kos 1 0.0182

4 Budi Kos 1 0.0182

5 Sinta Kos 1 0.0182

6 Aldian Kos 1 0.0182

7 Doni Kos 4 0.0727

8 Munawar Kos 4 0.0727

9 Andreas Kos 1 0.0182

10 Harry Kos 4 0.0182

11 Rika Kos 1 0.0182

12 Riski Kos 4 0.0727

13 Dedi Kos 1 0.0182

14 Dede Kos 1 0.0182

15 Anang Kos 1 0.0182

16 Mariya Kos 4 0.0727

17 Slamet Kos 4 0.0727

18 Diding Kos 7 0.1273

19 Yanto Kos 4 0.0727

20 Nurul Kos 3 0.0545



No Alternative Facility value Eigen vector

∑ 55 1

The third criterion is security using equation (2). The calculation process is the same as finding the eigenvector

facility where the higher the security value, the better, so the calculation process is the value of each security divided

by the total security value of all Kos. Table 8 is the result of the calculation of the eigenvector security.

Table 8. Eigenvector Security

No Alternative Security Value Eigen vector



3 Deni Kos 1 0.0435

4 Budi Kos 1 0.0435



7 Doni Kos 1 0.0435



10 Harry Kos 1 0.0435

11 Rika Kos 1 0.0435

12 Riski Kos 1 0.0435

13 Dedi Kos 1 0.0435

14 Dede Kos 1 0.0435

15 Anang Kos 1 0.0435




19 Yanto Kos 1 0.0435

20 Nurul Kos 1 0.0435

∑ 23 1

The fourth criterion is the distance using the equation (2). The calculation process is the same as finding the

eigenvector price by calculating the reverse value by subtracting the total value of the distance by the value of the

distance of each Kos. After that, to find the eigenvector value, we divided the value of each reverse Kos distance by

the total reverse distance. Table 9 is the result of calculating the eigenvector distance.

Table 9. Eigenvector Distance

No Alternative Distance value Reverse distance Eigen vector

1 Mariyam Kos 10,000 253,700 0.05063569

2 Hariyono Kos 8,800 254,900 0.050875197

3 Deni Kos 10,000 253,700 0.05063569

4 Budi Kos 9,900 253,800 0.050655649

5 Sinta Kos 9,300 254,400 0.050775403

6 Aldian Kos 79,000 184,700 0.03686406

7 Doni Kos 9,500 254,200 0.050735485

8 Munawar Kos 10,000 253,700 0.05063569

9 Andreas Kos 10,000 253,700 0.05063569

10 Harry Kos 10,000 253,700 0.05063569

11 Rika Kos 10,000 253,700 0.05063569



No Alternative Distance value Reverse distance Eigen vector

12 Riski Kos 9,600 254,100 0.050715526

13 Dedi Kos 11,000 252,700 0.050436102

14 Dede Kos 10,000 253,700 0.05063569

15 Anang Kos 10,000 253,700 0.05063569

16 Mariya Kos 9,300 254,400 0.050775403

17 Slamet Kos 9,300 254,400 0.050775403

18 Diding Kos 8,000 255,700 0.051034868

19 Yanto Kos 10,000 253,700 0.05063569

20 Nurul Kos 10,000 253,700 0.05063569

∑ 263,700 5,010,300 1

The fifth criterion is the convenience, using the equation (2), the calculation process is the same as finding the

facility eigenvector. The result is in Table 10.

Table 10. Eigenvector convenience

No Alternative Convenience Value Eigen vector



3 Deni Kos 3 0.05

4 Budi Kos 3 0.05

5 Sinta Kos 3 0.05

6 Aldian Kos 3 0.05

7 Doni Kos 3 0.05



10 Harry Kos 3 0.05

11 Rika Kos 3 0.05

12 Riski Kos 3 0.05

13 Dedi Kos 3 0.05

14 Dede Kos 3 0.05

15 Anang Kos 3 0.05




19 Yanto Kos 3 0.05

20 Nurul Kos 3 0.05

∑ 60 1

The sixth criterion is the parking lot using the equation (2). The calculation process is the same as finding the

facility eigenvector. The result is in Table 11.

Table 11. Eigenvector Parking Lot

No Alternative Parking lot Value Eigen vector





No Alternative Parking lot Value Eigen vector

3 Deni Kos 1 0.0455

4 Budi Kos 1 0.0455



7 Doni Kos 1 0.0455



10 Harry Kos 1 0.0455

11 Rika Kos 1 0.0455

12 Riski Kos 1 0.0455

13 Dedi Kos 1 0.0455

14 Dede Kos 1 0.0455

15 Anang Kos 1 0.0455




19 Yanto Kos 1 0.0455

20 Nurul Kos 1 0.0455

∑ 22 1

The seventh criterion is the amount of space using the equation (2). The calculation process is the same as

searching for the eigenvector of facilities where the higher the value of the number of spaces, the better, so the

calculation process is the value of each number of rooms divided by the total value of the total number of rooms

for all Kos. Table 12 is the result of the calculation of the eigenvector of the number of spaces.

Table 12. Eigenvector Number of Spaces

No Alternative Number of Spaces Value Eigen vector



3 Deni Kos 2 0.0541

4 Budi Kos 1 0.0270



7 Doni Kos 2 0.0541



10 Harry Kos 3 0.0811

11 Rika Kos 1 0.0270

12 Riski Kos 2 0.0541

13 Dedi Kos 1 0.0270

14 Dede Kos 1 0.0270

15 Anang Kos 1 0.0270






No Alternative Number of Spaces Value Eigen vector

19 Yanto Kos 3 0.0811

20 Nurul Kos 1 0.0270

∑ 37 1

After obtaining each priority criterion, the eigenvector value of each cost was obtained, and the unweighted

supermatrix was compiled from all the eigenvector values from the previous calculation. After that the value of the

unweighted supermatrix was multiplied by the eigenvector from the results of the pairwise comparison matrix of

criteria weights to produce the weighted supermatrix value. The final step was to iterate over the weighted supermatrix

with itself so that the same value was obtained in each row for the limiting supermatrix process. To generate a global

priority value, we calculated the alternative data of the Kos multiplied by the global eigenvector criteria using equation

(6).

Global priority = (alternative eigenvector criteria 1 x eigencriteria 1) +

(alternative eigenvector criteria 2 x eigencriteria 2)..n (6)

To calculate the global priority for each Kos, first we collected the eigen values of the criteria in the

alternative like in the eigen values of the criteria in the Mariyam Kos alternative. See Table 13.

Table 13. Eigen of Mariyam Kos Alternative

Kode Kriteria Nilai Eigen Eigen Global

H 0.0495 0.25

F 0.0727 0.22

KA 0.0435 0.18

J 0.0506 0.14

KN 0.05 0.11

P 0.0909 0.07

R 0.0811 0.04

The calculation process used equation 6.

Mariyam kos Priority Value

= (0.0495 * 0.25) + (0.0727 * 0.22) + (0.0435 * 0.18) + (0.0506 * 0.14) + (0.05 * 0.11) + (0.0909 * 0.07)

+ (0.0811 * 0.04)

= 0.05721082

So, the priority value on the Mariyam Kos alternative is 0.05721082.

The results of calculations using global priorities can be seen in Table 14.

Table 14. Alternative calculation

No Alternatif H F KA J KN P R Eigen Prioritas

1 Mariyam Kos 0.0495 0.0727 0.0435 0.0506 0.05 0.0909 0.0811

x

0.25 0.05721082

2 Hariyono Kos 0.0497 0.0727 0.0435 0.0509 0.05 0.0455 0.0811 0.22 0.054014794

3 Deni Kos 0.0499 0.0182 0.0435 0.0506 0.05 0.0455 0.0541 0.18 0.043366705

4 Budi Kos 0.0502 0.0182 0.0435 0.0507 0.05 0.0455 0.0270 0.14 0.042504853

5 Sinta Kos 0.0501 0.0182 0.0870 0.0508 0.05 0.0455 0.0270 0.11 0.048690892

6 Aldian Kos 0.0504 0.0182 0.0435 0.0369 0.05 0.0455 0.0270 0.07 0.039084642

7 Doni Kos 0.0497 0.0727 0.0435 0.0507 0.05 0.0455 0.0541 0.04 0.05303392

8 Munawar Kos 0.0497 0.0727 0.0870 0.0506 0.05 0.0909 0.0811 0.063446024

9 Andreas Kos 0.0505 0.0182 0.0435 0.0506 0.05 0.0455 0.0270 0.042555236



No Alternatif H F KA J KN P R Eigen Prioritas

10 Harry Kos 0.0495 0.0727 0.0435 0.0506 0.05 0.0455 0.0811 0.053915366

11 Rika Kos 0.0506 0.0182 0.0435 0.0506 0.05 0.0455 0.0270 0.042578967

12 Riski Kos 0.0501 0.0727 0.0435 0.0507 0.05 0.0455 0.0541 0.053108034

13 Dedi Kos 0.0506 0.0182 0.0435 0.0504 0.05 0.0455 0.0270 0.042521159

14 Dede Kos 0.0504 0.0182 0.0435 0.0506 0.05 0.0455 0.0270 0.042539415

15 Anang Kos 0.0505 0.0182 0.0435 0.0506 0.05 0.0455 0.0270 0.042563146

16 Mariya Kos 0.0499 0.0727 0.0870 0.0508 0.05 0.0455 0.0541 0.059279104

17 Slamet Kos 0.0493 0.0727 0.0435 0.0508 0.05 0.0455 0.0811 0.053910742

18 Diding Kos 0.0495 0.1273 0.0435 0.0510 0.05 0.0455 0.0541 0.062751032

19 Yanto Kos 0.0494 0.0727 0.0435 0.0506 0.05 0.0455 0.0811 0.053891635

20 Nurul Kos 0.0506 0.0545 0.0435 0.0506 0.05 0.0455 0.0270 0.049033512

After obtaining the global priority, we then sorted the values from the highest to the lowest value, then normalized

them in the form of a percent so that the difference in values between alternative costs is more visible. The ranking

results can be seen in Table 15.

Table 15. Alternative calculation

No Alternatif Nilai Normalisasi

1 Munawar Kos 0,063446024 6,3446024

2 Diding Kos 0,062751032 6,2751032

3 Mariya Kos 0,059279104 5,9279104

4 Mariyam Kos 0,05721082 5,721082

5 Hariyono Kos 0,054014794 5,4014794

6 Harry Kos 0,053915366 5,3915366

7 Slamet Kos 0,053910742 5,3910742

8 Yanto Kos 0,053891635 5,3891635

9 Riski Kos 0,053108034 5,3108034

10 Doni Kos 0,05303392 5,303392

11 Nurul Kos 0,049033512 4,9033512

12 Sinta Kos 0,048690892 4,8690892

13 Deni Kos 0,043366705 4,3366705

14 Rika Kos 0,042578967 4,2578967

15 Anang Kos 0,042563146 4,2563146

16 Andreas Kos 0,042555236 4,2555236

17 Dede Kos 0,042539415 4,2539415

18 Dedi Kos 0,042521159 4,2521159

19 Budi Kos 0,042504853 4,2504853

20 Aldian Kos 0,039084642 3,9084642

B. Data Modelling

The table relations can be seen in Figure 3.



Figure 3. Data Modelling

Conclusion Based on the results of tests and analysis, the following conclusions can be drawn 1) The boarding house searching

process using the Analytic Network Process (ANP) method is dynamic, which means that the total priority can be

increased or decreased as needed. 2) The results of calculations show that the highest value is 6.55%, namely Munawar

Kos and then Diding Kos with a value of 6.52%. 3) When viewed as the chosen priority, the prices and facilities

owned by Munawar Kos and Doni Kos are the same. However, Munawar Kos was in first place, while Doni Kos was

in tenth place. From these results, the level of security of the boarding house is the determinant factor. Munawar Kos

has a better level of security when compared to Doni Kos, even though Doni Kos is closer than Munawar Kos.

Acknowledgement This research received funding from Universitas Mercu Buana through the Central Research Bureau.

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fuzzy logic algorithm and analytic network process (anp

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